Image capture devices, e.g. digital cameras, include image sensors. Each image sensor attempts to model human vision and perception. However, image sensors do not ‘see’ the world with the same spectral responsivities as the human eye. Moreover, imaging sensors respond differently when different lights illuminate the same surface. Humans, however, adapt their color response so that surfaces appear to be approximately the same despite differences in scene illumination. This adaptive brain response is referred to as “color constancy”.
Spectral normalization is used as part of digital image rendering as an illumination balancing technique to mimic “color constancy”. Thus, the image sensors try to transform the digital image pixel values such that when they are displayed they create the perception that the surfaces in the image (originally illuminated by the scene illuminant) are illuminated by the same and, hopefully, desirable rendering illuminant, such as daylight.
There are two general strategies for performing illuminant estimation. One approach is to use a fixed color correction matrix and statistics derived from each image to create the white balancing coefficients, e.g. “Gray World”, “Beige World”. A second method is to pre-compute a set of matrices for likely scene illuminants and to use statistics from the image to choose the best matrix (from the set of pre-computed matrices) for that particular image.
When using a “color transformation matrix”, it is assumed that the colors in the captured scene integrate to a neutral color, e.g. gray or beige. The average R, G, and B values are calculated for the neutral color. The pixels in the captured image are adjusted accordingly. Fixed color correction matrices fail to accurately render images that are single color images or dominant color images. Dominant color images are quite common as they include relatively large expanses of a single color, e.g. sky, water, or grass. In such scenes, it cannot be assumed that the overall image integrates to the neutral color.